考虑环境影响的多产品间歇化工过程优化排序

提出一种用于多产品厂排序的多目标优化模型 ,其目标函数同时考虑了总生产时间和废物对环境的影响 ,定义了关于废物的环境影响因子及决策因子 ,用以对总生产时间和废物及其对环境的影响进行权衡 .采用改进的模拟退火算法对具有不同决策因子和环境影响因子情况下的算例进行了求解 ,结果表明 ,该模型能够较好地反映环境因素在多产品厂排序问题中的影响 ,使排序结果达到生产时间和环境影响的综合最优     

单机多产品注塑过程的节能调度方法

为减小单机多产品注塑过程的生产总能耗,提出一种基于旅行商算法(TSP)和遗传算法(GA)的节能调度方法。研究了注塑生产总能耗的3个重要组成:产品切换能耗、过渡调整能耗和稳定生产能耗,建立了产品切换过渡的能耗模型。以单产平稳模态为节点、过渡模态为支路,建立了单机多产品过程生产总能耗的有向图模型,将单机多产品能耗优化问题转化为经典的TSP问题。采用基于遗传算法的多目标逐层优化与TSP路径寻优思想,搜索各个单产平稳生产下的最优操作参数以及多产品的最优生产顺序,以期降低生产总能耗。该方法可提高生产效率,降低生产能耗。应用研究结果验证了方法的可行性和有效性。     

芳烃抽提过程多目标优化

芳烃抽提是芳烃生产过程的重要环节,其生产调优对提高整个芳烃联合装置的效益具有重要意义。基于流程模拟及响应面分析方法,得到了芳烃抽提过程的产品纯度模型及能耗模型。建立了以产品纯度最大化及过程能耗最小化的多目标优化模型。提出了一种改进的自适应加权求和算法,并用于多目标优化模型的求解。求解结果表明新算法在Pareto最优解分布的均匀性上与原算法相当,但求解效率要高于原算法。给出了不同产品等级下的最佳操作参数,采用优化后的操作参数可有效地提高产品纯度并降低过程能耗。提出的多目标优化模型及求解算法用于芳烃抽提过程的操作调优,可有效地提高决策的准确性。     

一种生态工业园区碳-氢-氧共生网络的多目标优化与决策方法

针对生态工业园区中碳-氢-氧共生网络,基于多尺度原子标定方法,同时考虑经济和环境因素及回用副产碳氢氧化合物,本工作提出了一种多目标优化及决策的方法,从备选方案中获得最优方案。该方法采用数学规划法,分别以最小总年度成本和二氧化碳年度排放量为目标函数进行全局优化,建立了混合整数非线性规划模型;采用ε-约束法,将二氧化碳年度排放量转化为约束条件,得到了总年度成本与二氧化碳年度排放量帕累托前沿,发现总年度成本与二氧化碳排放量成反比;采用多维偏好分析的线性规划和逼近于理想解的排序决策方法对帕累托前沿进行最优决策,发现两者选择同一点作为最优决策。基于所提出的方法对某工业园区进行优化,结果表明,合理利用现有副产碳氢氧化合物,可以减少原材料成本,从而使总年度成本和二氧化碳年度排放量分别减少了63.44%和76.99%。     

高分子注塑成型产品质量关键工艺参数的多目标优化设计

针对注塑成型(PIM)关键工艺参数操作不佳易引起产品质量缺陷问题，提出一种基于梯度增强型Kriging(GEK)模型的注塑工艺参数多目标优化设计方法。针对空调外罩注塑成型过程，根据工艺参数和质量指标开展七因子三水平的正交实验设计，并采用Moldflow软件进行模流分析计算，通过信噪比分析和方差分析选取影响产品质量的主要参数；基于GEK模型理论建立质量指标预测模型，采用多目标差分进化(MODE)算法以质量缺陷和周期时间最小化为目标搜寻全局最优解；将最优设计参数带回Moldflow软件进行模拟验证。结果表明翘曲量、体积收缩率和周期时间分别降低0.88%、4.68%和14.81%，大幅度提升了产品质量和生产效率。     

基于NSGA-Ⅱ算法的高炉生产配料多目标优化模型建立

高炉炼铁是一种典型的高能耗、高排放、高污染工业,合理的配料方案对高炉节能减排至关重要。基于高炉炼铁过程中的物质与能量守恒和高炉炉料结构理论,建立了以最小化生产成本和CO₂排放量为目标函数的高炉生产配料多目标优化模型,该模型采用非支配排序多目标遗传算法Ⅱ(NSGA-Ⅱ)进行求解,最终得到高炉生产配料多目标优化问题的Pareto最优解集。并将所得到的最优解与柳钢实际生产数据进行比较,结果表明建立的模型能使成本和CO₂排放量都有相应程度的降低,验证了该模型及NSGA-Ⅱ算法的正确性。炉长可以根据该多目标优化结果针对不同的需求选择相应的炉料配比,实现更精确的操作。     

基于BP-NSGA-Ⅱ的汽车仪表板注塑工艺优化

根据大型注塑件产品汽车仪表板的生产质量和成本都有较高要求的特点,以某汽车仪表板为研究对象,研究其注塑工艺参数对体积收缩率和翘曲变形量的影响并进行工艺参数的优化。利用Moldflow软件对其进行数值模拟仿真,采用Box-Behnken试验设计方法进行数据采集,以开模时间、模具温度、注塑时间、熔体温度、速度压力切换、保压压力、保压恒压时间、保压衰减时间、冷却时间9个工艺参数为影响因子,体积收缩率和翘曲变形量为优化目标,运用BP神经网络模型和非支配排序遗传算法(NSGA-Ⅱ)获得最佳工艺参数组合,结合Moldflow仿真验证了最佳工艺参数组合的准确性,最终两个目标值分别降低了8.58%和8.83%,汽车仪表板的成型质量得到了有效提高。     

MTO烯烃分离过程的多目标操作优化

现代煤化工中，甲醇制烯烃 （MTO） 是一个非常重要的装置。其烯烃分离过程面临着原料变动大、烯烃产品损失以及较高的公用工程消耗等问题。这就需要在满足产品规格和需求的情况下，优化操作条件以实现最大效益。以Lummus前脱丙烷的烯烃分离工艺为研究对象，以增加乙烯与丙烯的总收率和降低总能耗为优化目标，对该工艺流程进行建模模拟与多目标优化。采用非支配排序遗传算法（NSGA-II）进行多目标优化的求解，实现了15个操作变量的同时优化。在维持产品收率不变的前提下，可通过降低脱丙烷塔、脱乙烷塔和1^#丙烯精馏塔的回流比等优化措施找到了当前最优操作点。结果表明，该最优操作点与现有操作点相比可降低20 MW能耗。通过对决策变量的综合分析，确定了不同目标权衡下对应的各个操作变量的优化区间，发现精馏塔可以在多个最佳操作区间内运行。     

初探芳烃抽提过程多目标优化策略

芳烃抽提是芳烃生产过程中的重要环节，对于提高整个芳烃联合装置的效益意义重大。建议以产品纯度最大化的流程方案，降低能耗损失，是芳烃抽提过程多目标优化的最优策略。本文就芳烃抽提过程多目标优化策略进行分析，提出优化模型及算法，以此提高决策的准确性。     

基于非支配排序遗传算法的乙苯脱氢工艺条件优化

为提高现有乙苯脱氢制苯乙烯生产装置的生产率和节能水平,优化技术是一种有效的技术手段。基于改进的非支配排序遗传算法(NSGA-Ⅱ)研究了乙苯脱氢工艺条件的优化问题。把乙苯脱氢反应过程的转化率、选择性作为优化目标,动力学模型以及实际生产状况作为约束条件,构造乙苯脱氢过程的多目标优化问题。基于NSGA-Ⅱ算法求解得到的优化问题的Pareto最优集,分析了各个操作条件对乙苯脱氢生产过程转化率和选择性的影响,最后利用模糊综合评价法,为合理决策提供了有效的依据。结果表明NSGA-Ⅱ具有良好的全局优化性能,运用该算法可在不同的操作约束条件下,求解得到相应的满意解。     

Rule-based scheduling of multi-stage multi-product batch plants with parallel units

A novel rule-based model for multi-stage multi-product scheduling problem (MMSP) in batch plants with parallel units is proposed. The scheduling problem is decomposed into two sub-problems of order assignment and order sequencing. Firstly, hierarchical scheduling strategy is presented for solving the former sub-problem, where the multi-stage multi-product batch process is divided into multiple sequentially connected single process stages, and then the production of orders are arranged in each single stage by using forward order assignment strategy and backward order assignment strategy respectively according to the feature of scheduling objective. Line-up competition algorithm (LCA) is presented to find out optimal order sequence and order assignment rule, which can minimize total flow time or maximize total weighted process time. Computational results show that the proposed approach can obtain better solutions than those of the literature for all scheduling problems with more than 10 orders. Moreover, with the problem size increasing, the solutions obtained by the proposed approach are improved remarkably. The proposed approach has the potential to solve large size MMSP.     

A hybrid system of heuristics and simulated annealing for large size scheduling problems of batch processes with pipeless intermediate storage

This paper describes a new scheduling solution for large number multi-product batch processes with complex intermediate storage system. Recently many batch chemical industries have turned their attention to a more efficient system known as a pipeless batch system. But existing plants need to change their systems to pipeless systems, piece by piece. In this case, current systems are changed to pipeless systems by way of non critical process operations such as through the use of intermediate storage. We have taken the conventional batch plant with a pipeless storage system as an objective process. Although the operation of a pipeless storage system becomes more complex, its efficiency is very high. With this system, all of the storage should be commonly used by any batch unit. For this reason, solving the optimal scheduling problem of this system with a mathematical method is very difficult. Despite the attempts of many previous researches, there has been no contribution which solves the scheduling of intermediate storage for complex batch processes. In this paper, we have developed a hybrid system of heuristics and Simulated Annealing (SA) for large multi-product processes using a pipeless storage system. The results of this study show that the performance and computational time of this method are superior to that of SA and Rapid Access Extensive Search (RAES) methods.     

Short-term scheduling and recipe optimization of blending processes

The main objective of this paper is to develop an integrated approach to coordinate short-term scheduling of multi-product blending facilities with nonlinear recipe optimization. The proposed strategy is based on a hierarchical concept consisting of three business levels: Long-range planning, short-term scheduling and process control. Long-range planning is accomplished by solving a large-scale nonlinear recipe optimization problem (multi-blend problem). Resulting blending recipes and production volumes are provided as goals for the scheduling level. The scheduling problem is formulated as a mixed-integer linear program derived from a resource-task network representation. The scheduling model permits recipe changeovers in order to utilize an additional degree of freedom for optimization. By interpreting the solution of the scheduling problem, new constraints can be imposed on the previous multi-blend problem. Thus bottlenecks arising during scheduling are considered already on the topmost long-range planning level. Based on the outlined approach a commercial software system has been designed to optimize the operation of in-line blending and batch blending processes. The application of the strategy and software is demonstrated by a detailed case study.     

Genetic algorithm for large-size multi-stage batch plant scheduling

This paper presents a heuristic approach based on genetic algorithm (GA) for solving large-size multi-stage multi-product scheduling problem (MMSP) in batch plant. The proposed approach is suitable for different scheduling objectives, such as total process time, total flow time, etc. In the algorithm, solutions to the problem are represented by chromosomes that will be evolved by GA. A chromosome consists of order sequences corresponding to the processing stages. These order sequences are then assigned to processing units according to assignment strategies such as forward or backward assignment, active scheduling technique or similar technique, and some heuristic rules. All these measures greatly reduce unnecessary search space and increase the search speed. In addition, a penalty method for handling the constraints in the problem, e.g., the forbidden changeovers, is adopted, which avoids the infeasibility during the GA search and further greatly increases the search speed.     

新的多产品间歇生产调度的MILP模型

提出了一种新的多产品厂间歇调度问题的连续时间混合整数线性规划 (MILP)模型,该模型的整数变量体系不依赖于时间块(或者事件点)的概念,并且利用了变量物理概念上的对称互补性,使得与传统的建模方法相比不仅整数变量的数目减少了一半以上,而且建模思想、建模理论都有了新的改进.通过对一个算例的考察证实了新模型可以快速地求得全局最优解.     

有并行设备的多目的间歇生产调度

针对有并行设备的多目的间歇生产调度问题,建立了以所有订单生产步骤排序和订单各阶段所选用分配规则为决策变量的调度优化模型,并提出了列队竞争算法求解该模型的有效个体表示方法及变异操作策略。实例计算表明:所提出方法的求解效率优于文献中所报道的方法,特别在求解有多台并行设备的调度问题时能得到比文献更好的结果,表现出其求解大规模复杂多目的调度问题的潜力。     

A rule-based genetic algorithm for the scheduling of single-stage multi-product batch plants with parallel units

This paper presents a heuristic rule-based genetic algorithm (GA) for large-size single-stage multi-product scheduling problems (SMSP) in batch plants with parallel units. SMSP have been widely studied by the researchers. Most of them used mixed-integer linear programming (MILP) formulation to solve the problems. With the problem size increasing, the computational effort of MILP increases greatly. Therefore, it is very difficult for MILP to obtain acceptable solutions to large-size problems within reasonable time. To solve large-size problems, the preferred method in industry is the use of scheduling rules. However, due to the constraints in SMSP, the simple rule-based method may not guarantee the feasibility and quality of the solution. In this study, a random search based on heuristic rules was proposed first. Through exploring a set of random solutions, better feasible solutions can be achieved. To improve the quality of the random solutions, a genetic algorithm-based on heuristic rules has been proposed. The heuristic rules play a very important role in cutting down the solution space and reducing the search time. Through comparative study, the proposed method demonstrates promising performance in solving large-size SMSP.     

基于遗传算法求解以最大利润为目标的间歇生产过程调度问题

The optimal scheduling of multi-product batch process is studied and a new mathematics model targeting the maximum profit is proposed, which can be solved by the modified genetic algorithm (MGA) with mixed coding (sequence coding and decimal coding) developed by us. In which, the partially matched cross over (PMX) and reverse mutation are used for the sequence coding, whereas the arithmetic crossover and heteropic mutation are used for the decimal coding. In axidition, the relationship between production scale and production cost is analyzed and the maximum profit is always a trade-off of the production scale and production cost. Two examples are solved to demonstrate the effectiveness of the method.